Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation

2021 ◽  
Author(s):  
Zeng-Lei He ◽  
Jun-Bin Zhou ◽  
Zhi-Kun Liu ◽  
Si-Yi Dong ◽  
Yun-Tao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Acute Kidney Injury ◽  
Cardiac Death ◽  
Kidney Injury ◽  
Donation After Cardiac Death ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Cheng Qu ◽  
Lin Gao ◽  
Xian-qiang Yu ◽  
Mei Wei ◽  
Guo-quan Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Acute Pancreatitis ◽  
Logistic Regression ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Machine Learning Models

Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.

scholarly journals Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiawei He ◽  
Jin Lin ◽  
Meili Duan
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Intensive Care ◽  
Kidney Injury ◽  
Learning Models ◽  
Short Term ◽  
Acute Kidney Disease ◽  
Mimic Iii ◽  
Machine Learning Models

Background: Sepsis-associated acute kidney injury (AKI) is frequent in patients admitted to intensive care units (ICU) and may contribute to adverse short-term and long-term outcomes. Acute kidney disease (AKD) reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models to predict the occurrence of AKD in patients with sepsis-associated AKI.Methods: Using clinical data from patients with sepsis in the ICU at Beijing Friendship Hospital (BFH), we studied whether the following three machine learning models could predict the occurrence of AKD using demographic, laboratory, and other related variables: Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), decision trees, and logistic regression. In addition, we externally validated the results in the Medical Information Mart for Intensive Care III (MIMIC III) database. The outcome was the diagnosis of AKD when defined as AKI prolonged for 7–90 days according to Acute Disease Quality Initiative-16.Results: In this study, 209 patients from BFH were included, with 55.5% of them diagnosed as having AKD. Furthermore, 509 patients were included from the MIMIC III database, of which 46.4% were diagnosed as having AKD. Applying machine learning could successfully achieve very high accuracy (RNN-LSTM AUROC = 1; decision trees AUROC = 0.954; logistic regression AUROC = 0.728), with RNN-LSTM showing the best results. Further analyses revealed that the change of non-renal Sequential Organ Failure Assessment (SOFA) score between the 1st day and 3rd day (Δnon-renal SOFA) is instrumental in predicting the occurrence of AKD.Conclusion: Our results showed that machine learning, particularly RNN-LSTM, can accurately predict AKD occurrence. In addition, Δ SOFAnon−renal plays an important role in predicting the occurrence of AKD.

scholarly journals Calibration drift in regression and machine learning models for acute kidney injury

2017 ◽  
Vol 24 (6) ◽  
pp. 1052-1061 ◽  
Author(s):  
Sharon E Davis ◽  
Thomas A Lasko ◽  
Guanhua Chen ◽  
Edward D Siew ◽  
Michael E Matheny
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Acute Kidney Injury ◽  
Regression Models ◽  
Kidney Injury ◽  
Learning Models ◽  
Personalized Risk ◽  
Support Decision Making ◽  
Over Time ◽  
Machine Learning Models

Abstract Objective Predictive analytics create opportunities to incorporate personalized risk estimates into clinical decision support. Models must be well calibrated to support decision-making, yet calibration deteriorates over time. This study explored the influence of modeling methods on performance drift and connected observed drift with data shifts in the patient population. Materials and Methods Using 2003 admissions to Department of Veterans Affairs hospitals nationwide, we developed 7 parallel models for hospital-acquired acute kidney injury using common regression and machine learning methods, validating each over 9 subsequent years. Results Discrimination was maintained for all models. Calibration declined as all models increasingly overpredicted risk. However, the random forest and neural network models maintained calibration across ranges of probability, capturing more admissions than did the regression models. The magnitude of overprediction increased over time for the regression models while remaining stable and small for the machine learning models. Changes in the rate of acute kidney injury were strongly linked to increasing overprediction, while changes in predictor-outcome associations corresponded with diverging patterns of calibration drift across methods. Conclusions Efficient and effective updating protocols will be essential for maintaining accuracy of, user confidence in, and safety of personalized risk predictions to support decision-making. Model updating protocols should be tailored to account for variations in calibration drift across methods and respond to periods of rapid performance drift rather than be limited to regularly scheduled annual or biannual intervals.

scholarly journals Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246306
Author(s):  
Jialin Liu ◽  
Jinfa Wu ◽  
Siru Liu ◽  
Mengdie Li ◽  
Kunchang Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Kidney Injury ◽  
The Other ◽  
Receiver Operating Curve ◽  
Support Vector ◽  
Research Database ◽  
Learning Models ◽  
Risk Of Death ◽  
Machine Learning Models

Purpose The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive care unit), and to compare its performance with that of three other machine learning models. Methods We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), and RF (random forest). In the model comparison, the AUROC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model. Results A total of 7548 AKI patients were analyzed in this study. The overall in-hospital mortality of AKI patients was 16.35%. The best performing algorithm in this study was XGBoost with the highest AUROC (0.796, p < 0.01), F1(0.922, p < 0.01) and accuracy (0.860). The precision (0.860) and recall (0.994) of the XGBoost model rank second among the four models. Conclusion XGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death.

scholarly journals Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction

Healthcare ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1662
Author(s):  
Tao Han Lee ◽  
Jia-Jin Chen ◽  
Chi-Tung Cheng ◽  
Chih-Hsiang Chang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Kidney Injury ◽  
Prediction Models ◽  
Kidney Injury ◽  
Clinical Decision ◽  
Current Evidence ◽  
Learning Models ◽  
Definition Of ◽  
Machine Learning Models

Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients. Current guidelines use serum creatinine level and urine output rate for defining AKI and as the staging criteria of AKI. However, because they are not sensitive or specific markers of AKI, clinicians find it difficult to predict the occurrence of AKI and prescribe timely treatment. Advances in computing technology have led to the recent use of machine learning and artificial intelligence in AKI prediction, recent research reported that by using electronic health records (EHR) the AKI prediction via machine-learning models can reach AUROC over 0.80, in some studies even reach 0.93. Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised. Then, we summarize the current evidence regarding the application of e-alert systems and machine-learning models in AKI prediction.

scholarly journals Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model

2018 ◽  
Vol 7 (11) ◽  
pp. 428 ◽  
Author(s):  
Hyung-Chul Lee ◽  
Soo Yoon ◽  
Seong-Mi Yang ◽  
Won Kim ◽  
Ho-Geol Ryu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Liver Transplantation ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
Gradient Boosting Machine

Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.

scholarly journals Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Antonin Dauvin ◽  
Carolina Donado ◽  
Patrik Bachtiger ◽  
Ke-Chun Huang ◽  
Christopher Martin Sauer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Impaired Renal Function ◽  
Kidney Injury ◽  
Learning Models ◽  
Intensive Care Patients ◽  
Using Data ◽  
Machine Learning Models ◽  
Baseline Hemoglobin

AbstractPatients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86–0.89) classify an individual patient’s baseline hemoglobin and creatinine levels. Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.

MO353MACHINE LEARNING-BASED PREDICTION OF ACUTE KIDNEY INJURY AFTER NEPHRECTOMY IN PATIENTS WITH RENAL CELL CARCINOMA

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Sejoong Kim ◽  
Yeonhee Lee ◽  
Seung Seok Han
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Kidney Injury ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Learning Models ◽  
Scoring Model ◽  
Postoperative Aki ◽  
Machine Learning Models ◽  
Better Than

Abstract Background and Aims The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. Method A total of 4,104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. Machine learning models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Results Postoperative AKI developed in 1,167 patients (28.4%). All the machine learning models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the machine learning models than the LR-based scoring model over all the ranges of threshold probabilities. The LightGBM and random forest models, but not others, were well calibrated. Conclusion The application of machine learning algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

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